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A review on multiple sequence alignment from the perspective of genetic algorithm.

Biswanath Chowdhury1, Gautam Garai2

  • 1Department of Biophysics, Molecular Biology and Bioinformatics, University of Calcutta, Kolkata, WB, 700009, India.

Genomics
|July 4, 2017
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Summary

This study explores advanced genetic algorithms for multiple sequence alignment, a complex bioinformatics task. Recent trends show significant progress in improving protein alignment accuracy using these stochastic methods.

Keywords:
Genetic algorithmMultiobjective functionMultiple sequence alignment

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Sequence alignment is fundamental for understanding DNA, RNA, and protein functions.
  • Protein alignment is computationally complex, with existing methods yielding varied results.
  • Accurate alignment is crucial for downstream analyses like phylogenetic and structural predictions.

Purpose of the Study:

  • To review various methods applied in sequence alignment.
  • To highlight recent advancements in multiobjective genetic algorithms for multiple sequence alignment.
  • To discuss the ongoing research trends in improving alignment accuracy.

Main Methods:

  • Exploration of diverse sequence alignment techniques.
  • Focus on stochastic methods, particularly Genetic Algorithms.
  • Analysis of multiobjective genetic algorithm approaches for multiple sequence alignment.

Main Results:

  • Identified different types of alignment methods.
  • Showcased recent trends in multiobjective genetic algorithms for multiple sequence alignment.
  • Confirmed considerable progress in achieving higher alignment accuracy.

Conclusions:

  • Genetic Algorithms offer promising stochastic approaches for complex alignment problems.
  • Multiobjective optimization enhances the accuracy of multiple sequence alignment.
  • Continued research in this area is yielding significant improvements in bioinformatics.